The Positron emission tomography (PET) is a medical imaging technology based on nuclear physics and molecular biology. It can observe cells metabolism at molecular level, and provide effective basis for early detection of diseases especially tumors or cancers. In principle, the process of the PET imaging is to imaging the concentration distribution of the medicine in a patient's body. The radioactive isotope tracing drugs (tracers) injected into a patient's body enter into circulation system through blood and these drugs form a certain concentration distribution in different organs of the body. Since the half-time of the radioactive tracer is short and extremely unstable, the decay of the tracer would happen quickly and create a positron. This positron would be annihilated with the electron around, and it would create a pair of gamma photons with equally energy of 511 keV in the opposite direction. These photon pairs are detected by the detecting rings, then the photon pairs with radioactive drugs distribution data are processed by the coincidence process module to create the sinogram data. After that, the radioactive substance spatial concentration distribution in the body is reestablished by using corresponding mathematical methods to inverse and solve the sonogram data.
In recent years, PET imaging is increasingly and widely used in applied medical field. While at the same time, the clinical requirement for PET imaging has become higher. Higher spatial resolutions and real time scanning of patients are increasingly required in PET imaging in more and more medical fields. The increasing dimension of images and significant amount of data has become a challenge for the present reconstruction algorithms. In addition, these requirements also results in a higher requirement for the computer calculation capacity and memory.
At present, dynamic image reconstruction approaches largely fall into two groups: math analysis method and traditional iterative method. The first one mainly includes filtered back projection (FBP). It could obtain the reconstruction images in a short time but the accuracy of the reestablished images is not high enough and has serious artifacts. The other group includes the most frequently used Maximum Likelihood and Expectation. Maximization (ML-EM) method. This method improves the spatial resolution of the reconstructed images, but there are still serious artifacts in the reconstructed images. In addition, both of these methods consider the each frame as being independent from other frames. Therefore, the reconstructed images could not prove the time-relationship in dynamic PET images; besides neither of them are able to extract the target region form the background noise. Thus neither of them is robust to deal with interference of the noise data.